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Flashcard 1731707669772
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Parent (intermediate) annotation
Open itis a mathematical model of a financial market containing derivative investment instruments. From the partial differential equation in the model, known as the Black–Scholes equation, one can deduce the Black–Scholes formula, which gives <span>a theoretical estimate of the price of European-style options and shows that the option has a unique price regardless of the risk of the security and its expected return (instead replacing the security's expected return with the risk-neutral rate)
Original toplevel document
Black–Scholes model - WikipediaBlack–Scholes model - Wikipedia Black–Scholes model From Wikipedia, the free encyclopedia (Redirected from Black–Scholes) Jump to: navigation, search The Black–Scholes /ˌblæk ˈʃoʊlz/ [1] or Black–Scholes–Merton model is a mathematical model of a financial market containing derivative investment instruments. From the partial differential equation in the model, known as the Black–Scholes equation, one can deduce the Black–Scholes formula, which gives a theoretical estimate of the price of European-style options and shows that the option has a unique price regardless of the risk of the security and its expected return (instead replacing the security's expected return with the risk-neutral rate). The formula led to a boom in options trading and provided mathematical legitimacy to the activities of the Chicago Board Options Exchange and other options markets around the world. [2]
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Morphism - Wikipedia
st of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (April 2016) (Learn how and when to remove this template message) <span>In many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another. The notion of morphism recurs in much of contemporary mathematics. In set theory, morphisms are functions; in linear algebra, linear transformations; in group theory, group homomorphisms; in topology, continuous functions, and so on. In category theory, morphism is a broadly similar idea, but somewhat more abstract: the mathematical objects involved need not be sets, and the relationship between them may be someth
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Parent (intermediate) annotation
Open itIn many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another. The notion of morphism recurs in much of contemporary mathematics. In set theory, morphisms are functions; in linear algebra, linear transformations; in group theory, group homomorphism
Original toplevel document
Morphism - Wikipediast of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (April 2016) (Learn how and when to remove this template message) <span>In many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another. The notion of morphism recurs in much of contemporary mathematics. In set theory, morphisms are functions; in linear algebra, linear transformations; in group theory, group homomorphisms; in topology, continuous functions, and so on. In category theory, morphism is a broadly similar idea, but somewhat more abstract: the mathematical objects involved need not be sets, and the relationship between them may be someth
Flashcard 1732635921676
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Parent (intermediate) annotation
Open itIn many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another.
Original toplevel document
Morphism - Wikipediast of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (April 2016) (Learn how and when to remove this template message) <span>In many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another. The notion of morphism recurs in much of contemporary mathematics. In set theory, morphisms are functions; in linear algebra, linear transformations; in group theory, group homomorphisms; in topology, continuous functions, and so on. In category theory, morphism is a broadly similar idea, but somewhat more abstract: the mathematical objects involved need not be sets, and the relationship between them may be someth
Flashcard 1732637494540
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Parent (intermediate) annotation
Open itIn many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another.
Original toplevel document
Morphism - Wikipediast of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. (April 2016) (Learn how and when to remove this template message) <span>In many fields of mathematics, morphism refers to a structure-preserving map from one mathematical structure to another. The notion of morphism recurs in much of contemporary mathematics. In set theory, morphisms are functions; in linear algebra, linear transformations; in group theory, group homomorphisms; in topology, continuous functions, and so on. In category theory, morphism is a broadly similar idea, but somewhat more abstract: the mathematical objects involved need not be sets, and the relationship between them may be someth
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Topology - Wikipedia
ogy (disambiguation). For a topology of a topos or category, see Lawvere–Tierney topology and Grothendieck topology. [imagelink] Möbius strips, which have only one surface and one edge, are a kind of object studied in topology. <span>In mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing. This can be studied by considering a collection of subsets, called open sets, that satisfy certain properties, turning the given set into what is known as a topological space. Important
Flashcard 1741389171980
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Parent (intermediate) annotation
Open itIn mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing.
Original toplevel document
Topology - Wikipediaogy (disambiguation). For a topology of a topos or category, see Lawvere–Tierney topology and Grothendieck topology. [imagelink] Möbius strips, which have only one surface and one edge, are a kind of object studied in topology. <span>In mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing. This can be studied by considering a collection of subsets, called open sets, that satisfy certain properties, turning the given set into what is known as a topological space. Important
Flashcard 1741390744844
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Parent (intermediate) annotation
Open itIn mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing.
Original toplevel document
Topology - Wikipediaogy (disambiguation). For a topology of a topos or category, see Lawvere–Tierney topology and Grothendieck topology. [imagelink] Möbius strips, which have only one surface and one edge, are a kind of object studied in topology. <span>In mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing. This can be studied by considering a collection of subsets, called open sets, that satisfy certain properties, turning the given set into what is known as a topological space. Important
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Norm (mathematics) - Wikipedia
ositive length or size to each vector in a vector space—save for the zero vector, which is assigned a length of zero. A seminorm, on the other hand, is allowed to assign zero length to some non-zero vectors (in addition to the zero vector). <span>A norm must also satisfy certain properties pertaining to scalability and additivity which are given in the formal definition below. A simple example is two dimensional Euclidean space R 2 equipped with the "Euclidean norm" (see below) Elements in this vector space (e.g., (3, 7)) are usually drawn as arr
Flashcard 1744136703244
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Parent (intermediate) annotation
Open itA norm must also satisfy certain properties pertaining to scalability and additivity which are given in the formal definition below.
Original toplevel document
Norm (mathematics) - Wikipediaositive length or size to each vector in a vector space—save for the zero vector, which is assigned a length of zero. A seminorm, on the other hand, is allowed to assign zero length to some non-zero vectors (in addition to the zero vector). <span>A norm must also satisfy certain properties pertaining to scalability and additivity which are given in the formal definition below. A simple example is two dimensional Euclidean space R 2 equipped with the "Euclidean norm" (see below) Elements in this vector space (e.g., (3, 7)) are usually drawn as arr
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Matrix (mathematics) - Wikipedia
x. In complex matrices, symmetry is often replaced by the concept of Hermitian matrices, which satisfy A ∗ = A, where the star or asterisk denotes the conjugate transpose of the matrix, that is, the transpose of the complex conjugate of A. <span>By the spectral theorem, real symmetric matrices and complex Hermitian matrices have an eigenbasis; that is, every vector is expressible as a linear combination of eigenvectors. In both cases, all eigenvalues are real. [29] This theorem can be generalized to infinite-dimensional situations related to matrices with infinitely many rows and columns, see below. Invertible matrix and its inverse[edit s
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Parent (intermediate) annotation
Open itBy the spectral theorem, real symmetric matrices and complex Hermitian matrices have an eigenbasis; that is, every vector is expressible as a linear combination of eigenvectors. In both cases, all eigenvalues are real.
Original toplevel document
Matrix (mathematics) - Wikipediax. In complex matrices, symmetry is often replaced by the concept of Hermitian matrices, which satisfy A ∗ = A, where the star or asterisk denotes the conjugate transpose of the matrix, that is, the transpose of the complex conjugate of A. <span>By the spectral theorem, real symmetric matrices and complex Hermitian matrices have an eigenbasis; that is, every vector is expressible as a linear combination of eigenvectors. In both cases, all eigenvalues are real. [29] This theorem can be generalized to infinite-dimensional situations related to matrices with infinitely many rows and columns, see below. Invertible matrix and its inverse[edit s
Flashcard 1749126352140
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Parent (intermediate) annotation
Open itBy the spectral theorem, real symmetric matrices and complex Hermitian matrices have an eigenbasis
Original toplevel document
Matrix (mathematics) - Wikipediax. In complex matrices, symmetry is often replaced by the concept of Hermitian matrices, which satisfy A ∗ = A, where the star or asterisk denotes the conjugate transpose of the matrix, that is, the transpose of the complex conjugate of A. <span>By the spectral theorem, real symmetric matrices and complex Hermitian matrices have an eigenbasis; that is, every vector is expressible as a linear combination of eigenvectors. In both cases, all eigenvalues are real. [29] This theorem can be generalized to infinite-dimensional situations related to matrices with infinitely many rows and columns, see below. Invertible matrix and its inverse[edit s
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Norm (mathematics) - Wikipedia
analysis. For field theory, see Field norm. For ideals, see Ideal norm. For group theory, see Norm (group). For norms in descriptive set theory, see prewellordering. In linear algebra, functional analysis, and related areas of mathematics, <span>a norm is a function that assigns a strictly positive length or size to each vector in a vector space—save for the zero vector, which is assigned a length of zero. A seminorm, on the other hand, is allowed to assign zero length to some non-zero vectors (in addition to the zero vector).
Flashcard 1753207409932
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Parent (intermediate) annotation
Open ita norm is a function that assigns a strictly positive length or size to each vector in a vector space
Original toplevel document
Norm (mathematics) - Wikipediaanalysis. For field theory, see Field norm. For ideals, see Ideal norm. For group theory, see Norm (group). For norms in descriptive set theory, see prewellordering. In linear algebra, functional analysis, and related areas of mathematics, <span>a norm is a function that assigns a strictly positive length or size to each vector in a vector space—save for the zero vector, which is assigned a length of zero. A seminorm, on the other hand, is allowed to assign zero length to some non-zero vectors (in addition to the zero vector).
Flashcard 1753209769228
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Parent (intermediate) annotation
Open ita norm is a function that assigns a strictly positive length or size to each vector (bar zero vector) in a vector space
Original toplevel document
Norm (mathematics) - Wikipediaanalysis. For field theory, see Field norm. For ideals, see Ideal norm. For group theory, see Norm (group). For norms in descriptive set theory, see prewellordering. In linear algebra, functional analysis, and related areas of mathematics, <span>a norm is a function that assigns a strictly positive length or size to each vector in a vector space—save for the zero vector, which is assigned a length of zero. A seminorm, on the other hand, is allowed to assign zero length to some non-zero vectors (in addition to the zero vector).
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Linear differential equation - Wikipedia
tml>Linear differential equation - Wikipedia Linear differential equation From Wikipedia, the free encyclopedia Jump to: navigation, search In mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form a 0 ( x ) y +
Flashcard 1758185524492
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Parent (intermediate) annotation
Open itIn mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives
Original toplevel document
Linear differential equation - Wikipediatml>Linear differential equation - Wikipedia Linear differential equation From Wikipedia, the free encyclopedia Jump to: navigation, search In mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form a 0 ( x ) y +
Flashcard 1758187097356
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Parent (intermediate) annotation
Open itIn mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives
Original toplevel document
Linear differential equation - Wikipediatml>Linear differential equation - Wikipedia Linear differential equation From Wikipedia, the free encyclopedia Jump to: navigation, search In mathematics, a linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form a 0 ( x ) y +
Flashcard 1758518185228
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| repetition number in this series | 0 | memorised on | scheduled repetition | ||||
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Parent (intermediate) annotation
Open itIn mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing.
Original toplevel document
Topology - Wikipediaogy (disambiguation). For a topology of a topos or category, see Lawvere–Tierney topology and Grothendieck topology. [imagelink] Möbius strips, which have only one surface and one edge, are a kind of object studied in topology. <span>In mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing. This can be studied by considering a collection of subsets, called open sets, that satisfy certain properties, turning the given set into what is known as a topological space. Important
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Lebesgue measure - Wikipedia
c, d] is Lebesgue measurable, and its Lebesgue measure is (b − a)(d − c), the area of the corresponding rectangle. Moreover, every Borel set is Lebesgue measurable. However, there are Lebesgue measurable sets which are not Borel sets. [3] [4] <span>Any countable set of real numbers has Lebesgue measure 0. In particular, the Lebesgue measure of the set of rational numbers is 0, although the set is dense in R. The Cantor set is an example of an uncountable set that has Lebesgue measure zer
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Lebesgue measure - Wikipedia
, because E {\displaystyle E} is a subset of the union of the intervals, and so the intervals may include points which are not in E {\displaystyle E} . <span>The Lebesgue outer measure emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets. Intuitively, it is the total length of those interval sets which fit E {\displaystyle E} most tightly and do not overlap. That characterize
Flashcard 1758533389580
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Parent (intermediate) annotation
Open itThe Lebesgue outer measure of a set E emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets (unions of open intervals that include E).
Original toplevel document
Lebesgue measure - Wikipedia, because E {\displaystyle E} is a subset of the union of the intervals, and so the intervals may include points which are not in E {\displaystyle E} . <span>The Lebesgue outer measure emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets. Intuitively, it is the total length of those interval sets which fit E {\displaystyle E} most tightly and do not overlap. That characterize
Flashcard 1758534962444
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Parent (intermediate) annotation
Open itThe Lebesgue outer measure of a set E emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets (unions of open intervals that include E).
Original toplevel document
Lebesgue measure - Wikipedia, because E {\displaystyle E} is a subset of the union of the intervals, and so the intervals may include points which are not in E {\displaystyle E} . <span>The Lebesgue outer measure emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets. Intuitively, it is the total length of those interval sets which fit E {\displaystyle E} most tightly and do not overlap. That characterize
Flashcard 1758536535308
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Parent (intermediate) annotation
Open itThe Lebesgue outer measure of a set E emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets (unions of open intervals that include E).
Original toplevel document
Lebesgue measure - Wikipedia, because E {\displaystyle E} is a subset of the union of the intervals, and so the intervals may include points which are not in E {\displaystyle E} . <span>The Lebesgue outer measure emerges as the greatest lower bound (infimum) of the lengths from among all possible such sets. Intuitively, it is the total length of those interval sets which fit E {\displaystyle E} most tightly and do not overlap. That characterize
Flashcard 1758538108172
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Parent (intermediate) annotation
Open itAny countable set of real numbers has Lebesgue measure 0.
Original toplevel document
Lebesgue measure - Wikipediac, d] is Lebesgue measurable, and its Lebesgue measure is (b − a)(d − c), the area of the corresponding rectangle. Moreover, every Borel set is Lebesgue measurable. However, there are Lebesgue measurable sets which are not Borel sets. [3] [4] <span>Any countable set of real numbers has Lebesgue measure 0. In particular, the Lebesgue measure of the set of rational numbers is 0, although the set is dense in R. The Cantor set is an example of an uncountable set that has Lebesgue measure zer
Flashcard 1758539681036
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-norm of vectors 
is 
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Norm (mathematics) - Wikipedia
x i {\displaystyle \sum _{i=1}^{n}x_{i}} is not a norm because it may yield negative results. p-norm[edit source] Main article: L p space <span>Let p ≥ 1 be a real number. The p {\displaystyle p} -norm (also called ℓ p {\displaystyle \ell _{p}} -norm) of vectors x = ( x 1 , … , x n ) {\displaystyle \mathbf {x} =(x_{1},\ldots ,x_{n})} is ‖ x ‖ p := ( ∑ i = 1 n | x i | p ) 1 / p . {\displaystyle \left\|\mathbf {x} \right\|_{p}:={\bigg (}\sum _{i=1}^{n}\left|x_{i}\right|^{p}{\bigg )}^{1/p}.} For p = 1 we get the taxicab norm, for p = 2 we get the Euclidean norm, and as p approaches ∞ {\displaystyle \infty } the p-norm approa
Flashcard 1759650385164
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Parent (intermediate) annotation
Open itLet p ≥ 1 be a real number. The -norm of vectors is
Original toplevel document
Norm (mathematics) - Wikipediax i {\displaystyle \sum _{i=1}^{n}x_{i}} is not a norm because it may yield negative results. p-norm[edit source] Main article: L p space <span>Let p ≥ 1 be a real number. The p {\displaystyle p} -norm (also called ℓ p {\displaystyle \ell _{p}} -norm) of vectors x = ( x 1 , … , x n ) {\displaystyle \mathbf {x} =(x_{1},\ldots ,x_{n})} is ‖ x ‖ p := ( ∑ i = 1 n | x i | p ) 1 / p . {\displaystyle \left\|\mathbf {x} \right\|_{p}:={\bigg (}\sum _{i=1}^{n}\left|x_{i}\right|^{p}{\bigg )}^{1/p}.} For p = 1 we get the taxicab norm, for p = 2 we get the Euclidean norm, and as p approaches ∞ {\displaystyle \infty } the p-norm approa
Flashcard 1759652744460
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Parent (intermediate) annotation
Open itLet p ≥ 1 be a real number. The -norm of vectors is
Original toplevel document
Norm (mathematics) - Wikipediax i {\displaystyle \sum _{i=1}^{n}x_{i}} is not a norm because it may yield negative results. p-norm[edit source] Main article: L p space <span>Let p ≥ 1 be a real number. The p {\displaystyle p} -norm (also called ℓ p {\displaystyle \ell _{p}} -norm) of vectors x = ( x 1 , … , x n ) {\displaystyle \mathbf {x} =(x_{1},\ldots ,x_{n})} is ‖ x ‖ p := ( ∑ i = 1 n | x i | p ) 1 / p . {\displaystyle \left\|\mathbf {x} \right\|_{p}:={\bigg (}\sum _{i=1}^{n}\left|x_{i}\right|^{p}{\bigg )}^{1/p}.} For p = 1 we get the taxicab norm, for p = 2 we get the Euclidean norm, and as p approaches ∞ {\displaystyle \infty } the p-norm approa